lightcurver: A Python Pipeline for Precise Photometry of Multiple-Epoch Wide-Field Images

Abstract

lightcurver is a photometric pipeline for time series astronomical imaging data, designed for the semi-automatic extraction of precise light curves from small, blended targets. Such targets include, but are not limited to, lensed quasars, supernovae, or Cepheids in crowded fields. lightcurver leverages STARRED (Michalewicz et al., 2023; Millon et al., 2024) to generate state-of-the-art empirical point spread function (PSF) models for each image. It then determines the relative zeropoints between epochs by combining the PSF-photometry fluxes of several stars in the field of view. Subsequently, STARRED is used again to simultaneously model the calibrated pixels of the region of interest across all epochs. This process yields light curves of the point sources and a high-resolution image model of the region of interest, cumulating the signal from all epochs. lightcurver aims to be maintainable, fast, and incremental in its processing approach. As such, it can enable the daily photometric analysis of a large number of blended targets in the context of the upcoming Rubin Observatory Legacy Survey of Space and Time.

5 pages

References in corpus (6)

• Array Programming with NumPy
• The Gaia mission
• The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
• astroquery: An Astronomical Web-Querying Package in Python
• Image deconvolution and PSF reconstruction with STARRED: a wavelet-based two-channel method optimized for light-curve extraction
• STARRED: a two-channel deconvolution method with Starlet regularization

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